Feature Selection Technique Using Multiple Linear Regression for Accurate Electricity Demand Forecasting

The rising power demand, driven by population growth, technological innovations, and the advent of smart cities, necessitates precise forecasting to ensure efficient energy distribution and align supply with demand. This paper presents a novel methodology for predicting short-term power consumption through machine learning approaches, specifically employing multiple linear regression for feature selection. In this study, two models are implemented and compared: Support Vector Regression (SVR) and Long-Short-Term Memory (LSTM). Exploratory data analysis was used to discover the relationships and associations between variables. It reveals that temperature, humidity, time of day, and season are major determinants of electricity use. The results indicate that the LSTM model surpasses Support Vector Regression (SVR) in terms of accuracy and precision. By incorporating multiple linear regression (MLR) for feature selection, the performance of both models improved, with precision gains of 29.1% for SVR and 18.19% for LSTM. Removing extraneous elements, such as wind speed and diffuse solar radiation, enhanced the models’ efficiency and interpretability, allowing for a focus on the most significant factors. The study’s findings underscore the need to optimize feature selection to enhance forecast accuracy and streamline models. This method provides critical insights for enhancing energy management strategies and facilitating sustainable power distribution in light of rising global energy demand.